Automatic simplification of large-scale reaction networks

Time
Monday, 28. October 2019
11:45 - 13:30

Location
M629

Organizer
Stefano Tognazzi, Centre for the Advanced Study of Collective Behaviour

Speaker:
Mirco Tribastone, IMT Lucca

This event is part of an event series „Seminar Series of CASCB“.

View the recording of Mirco's talk here

Mirco Tribastone is Associate Professor of Computer Science within the SysMA research unit of IMT Lucca. He is interested in the quantitative modeling and analysis of concurrent and distributed systems using mathematical tools such as stochastic processes (in particular Markov chains) and differential equations, as well as higher-level formalisms such as process algebra and queueing networks. A major general theme of his research is to develop effective techniques for the analysis of large-scale models where massive amounts of entities are involved.

You can read more about his work at SysMA Unit

Following the talk on October 28, there will be a chance to meet Mirco Tribastone at a light lunch (12:45 outside the seminar room). Participation in the lunch is only by registration. In the evening, there will also be a dinner, which you can register to attend by emailing Stefano Tognazzi

Automatic simplification of large-scale reaction networks

Reaction networks are a basic model for the quantitative analysis of collective behaviour in a wide range of natural and engineering disciplines, including chemistry, computer science, ecology, physics, and systems biology. Often, detailed mechanistic models of complex systems yield large-scale reaction networks. This introduces a major problem due to the significant computational costs for analysing the Markov chains (or the ordinary differential equations) that underlie the dynamical behaviour of a reaction network. In this talk I will review recent as well as ongoing work on techniques for the automatic simplification of reaction networks. I will show the effectiveness of our methods as well as their capability in providing physically interpretable patterns of aggregation emerging in a variety of models of signalling pathways, gene regulation networks, epidemiological processes, and opinion formation.